Clustering data with anisotropic distributions and complex local structures remains a significant challenge, particularly for models that rely on homogeneity assumptions. Such methods often struggle to capture directional variations, leading to blurred cluster boundaries and degraded performance on non-uniform or large-scale datasets. To address these limitations, we introduce EB-DP, a structure-adaptive clustering algorithm that integrates ellipsoidal ball modeling with a multi-scale hybrid splitting strategy. EB-DP constructs ellipsoidal granules through eigendecomposition of local covariance matrices, enabling alignment with the principal directions of data variation. A hierarchical splitting scheme combining axis-guided projection and two-means refinement ensures coarse-to-fine partitioning while preserving structural consistency. To improve cluster center identification and label assignment, during the clustering decision-making stage, a triple constraint mechanism incorporating ellipsoidal density, inter-ball distance, and axis-angle is introduced. Extensive experiments on synthetic and real-world datasets demonstrate that EB-DP generally achieves better ACC and NMI than representative baselines, while also reducing computational overhead.